• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.6C
• /
• pp.544-561
• /
• 2002

In this paper, we propose a new hybrid type of the routing protocol (Virtual Cluster-based Routing Protocol: VCRP) for mobile ad-hoc networks, based on a virtual cluster, which is defined as a narrow-sense network to exchange the basic information related to the routing among the adjacent nodes. This particular approach combines advantage of proactive routing protocol (PRP), which immediately provides the route collecting the network-wide topological and metric information, with that of reactive routing protocol, which relies on the route query packet to collect the route information on its way to the destination without exchanging any information between nodes. Furthermore, it also provides the back-up route as a byproduct, along with the optimal route, which leads to the VCBRP (Virtual Cluster-based Routing Protocol with Backup Route) establishing the alternative route immediately after a network topology is changed due to degradation of link quality and terminal mobility, Our simulation studies have shown that the proposed routing protocols are robust against dynamics of network topology while improving the performances of packet transfer delay, link failure ratio, and throughput over those of the existing routing protocols without much compromising the control overhead efficiency.

• Journal of Korean Society of Transportation
• /
• v.20 no.7
• /
• pp.177-183
• /
• 2002

Current state-of-the-art of VMS control is based upon simple knowledge-based inference engine with message set and each message's priority. And R&Ds of the VMS control are focused on the accurate detection and estimation of traffic condition of the subject roadways. However VMS display itself cannot achieve a desirable traffic allocation among alternative routes in the network In this context, VMS display strategy is the most crucial part in the VMS control. VMS itself has several limitations in its nature. It is generally known that VMS causes overreaction and concentration problems, which may be more serious in urban network than highway network because diversion should be more easily made in urban network. A feedback control algorithm is proposed in this paper to address the above-mentioned issues. It is generally true that feedback control approach requires low computational effort and is less sensitive to models inaccuracy and disturbance uncertainties. Major features of the proposed algorithm are as follows: Firstly, a regulator is designed to attain system optimal traffic allocation among alternative routes for each VMS in the network. Secondly, strategic messages should be prepared to realize the desirable traffic allocation, that is, output of the above regulator. VMS display strategy module is designed in this context. To evaluate Probable control benefit and to detect logical errors of the Proposed feedback algorithm, a offline simulation test is performed using real network in Daejon, Korea.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.4
• /
• pp.772-780
• /
• 2008

K-means is one of the simplest unsupervised learning algorithms that solve the clustering problem. However K-means suffers the basic shortcoming: the number of clusters k has to be known in advance. In this paper, we propose extensions of X-means, which can estimate the number of clusters using Bayesian information criterion(BIC). We introduce two different versions of algorithm: modified X-means(MX-means) and generalized X-means(GX-means), which employ one full covariance matrix for one cluster and so can estimate the number of clusters efficiently without severe over-fitting which X-means suffers due to its spherical cluster assumption. The algorithms start with one cluster and try to split a cluster iteratively to maximize the BIC score. The former uses K-means algorithm to find a set of optimal clusters with current k, which makes it simple and fast. However it generates wrongly estimated centers when the clusters are overlapped. The latter uses EM algorithm to estimate the parameters and generates more stable clusters even when the clusters are overlapped. Experiments with synthetic data show that the purposed methods can provide a robust estimate of the number of clusters and cluster parameters compared to other existing top-down algorithms.

• Korean Journal of Optics and Photonics
• /
• v.17 no.5
• /
• pp.383-390
• /
• 2006

The SharkHartmann wavefront sensors are the most popular devices to measure wavefront in the field of adaptive optics. The Shack-Hartmann sensors measure the centroids of spot irradiance distribution formed by each corresponding micro-lens. The centroids are linearly proportional to the local mean slopes of the wavefront defined within the corresponding sub-aperture. The wavefront is then reconstructed from the evaluated local mean slopes. The uncertainty of the Shack-Hartmann sensor is caused by various factors including the detector noise, the limited size of the detector, the magnitude and profile of spot irradiance distribution, etc. This paper investigates the noise propagation in two major centroid evaluation algorithms through computer simulation; 1st order moments of the irradiance algorithms i.e. center of gravity algorithm, and correlation algorithm. First, the center of gravity algorithm is shown to have relatively large dependence on the magnitudes of noises and the shape & size of irradiance sidelobes, whose effects are also shown to be minimized by optimal thresholding. Second, the correlation algorithm is shown to be robust over those effects, while its measurement accuracy is vulnerable to the size variation of the reference spot. The investigation is finally confirmed by experimental measurements of defocus wavefront aberrations using a Shack-Hartmann sensor using those two algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.4
• /
• pp.232-241
• /
• 2019

This study examines one of the conventional and controversial issues in modern finance. Specifically, this study identifies financial determinants of corporate R&D intensity for firms belonging to Korean Chaebols. Empirical estimation procedures are applied to derive more robust results of each hypothesis test. Static panel data, Tobit regression and stepwise regression models are employed to obtain significant financial factors of R&D expenditures, while logit, probit and complementary log-log regression models are used to detect financial differences between Chaebol firms and their counterparts not classified as Chaebols. Study results found the level of R&D intensity in the prior fiscal year, market-value based leverage ratio and firm size empirically showed their significance to account for corporate R&D intensity in the first hypothesis test, whereas the majority of explanatory variables had important power on a relative basis. Assuming that the current circumstances in the domestic capital market may necessitate gradual changes of Korean Chaebols in terms of their socio-economic function, the results of this study are expected to contribute to identifying financial antecedents that can be beneficial to attain optimal level of corporate R&D expenditures for Chaebol firms on a virtuous cycle.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.10
• /
• pp.373-380
• /
• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• Membrane Journal
• /
• v.32 no.1
• /
• pp.43-49
• /
• 2022

Solid-state supercapacitors with high safety and robust mechanical properties are attracting global attention as next-generation energy storage devices. As an electrode of a supercapacitor, an economical carbon-based electrode is widely used. However, when an aqueous electrolyte is introduced, the charge transfer resistance increases because the interfacial contact between the hydrophobic electrode surface and aqueous electrolyte is not good. In this regard, we propose a method to obtain higher electrochemical performance based on improved interfacial properties by treating the electrode surface with oxygen plasma. The surface hydrophilization induced by the enriched oxygen functionalities was confirmed by the contact angle measurement. As a result, the degree of hydrophilization was easily adjusted by controlling the power and duration of the oxygen plasma treatment. As the electrolyte of the supercapacitor, PVA/H₃PO₄, which is a typical solid-state aqueous electrolyte, was used. Free-standing membranes of PVA/H₃PO₄ electrolyte were prepared and then pressed onto the electrode. The optimal condition was to perform oxygen plasma treatment for 5 seconds with a low power of 15 W, and the energy density of the supercapacitor increased by about 8%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.7
• /
• pp.1951-1975
• /
• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• Journal of Korean Medicine Rehabilitation
• /
• v.33 no.3
• /
• pp.135-148
• /
• 2023

Objectives To investigate the effectiveness of acupuncture in the treatment of post-disaster musculoskeletal pain by reviewing relevant clinical studies. Methods A systematic search was conducted across 10 electronic databases to identify relevant clinical studies on acupuncture treatment for post-disaster musculoskeletal pain until May 2023. The methodological quality was evaluated using the Cochrane Risk of Bias 2 and Risk of Bias Assessment tool for non-randomized studies tools. Results Six articles were analyzed, including two randomized controlled trials (RCTs), two before-after studies, one qualitative research, and one case series. Overall, acupuncture therapy showed some improvement in pain scale among musculoskeletal pain survivors. However, no significant improvement was observed in the Short-Form McGill Pain Questionnaire (SF-MPQ-2). Subgroup analysis of participants who completed at least four acupuncture sessions revealed a significant effect on the SFMPQ-2. Additionally, a significant improvement in 36-Item Short Form Survey (SF36P) was observed after 6 months of treatment, but the 2-month treatment period did not show statistically significant effects on SF-36P improvement. The evaluation of the methodological quality of the RCTs identified some concerns of bias. Conclusions The results suggest that acupuncture is effective in alleviating post-disaster musculoskeletal pain. However, considering the limited number of selected studies and the inclusion of subjective evaluation measures, caution should be exercised in interpreting the results. Further large-scale follow-up studies are needed to determine the optimal frequency and duration of acupuncture treatment. Well-designed controlled trials should be conducted to provide more robust evidence regarding the effectiveness of acupuncture for post-disaster musculoskeletal pain.

• Smart Media Journal
• /
• v.12 no.1
• /
• pp.9-16
• /
• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.